Overview

Dataset statistics

Number of variables29
Number of observations13248
Missing cells454
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 MiB
Average record size in memory204.0 B

Variable types

Numeric11
Text5
Categorical7
Boolean5
DateTime1

Alerts

Distance_education has constant value ""Constant
Intensive_english_centre has constant value ""Constant
School_gender has constant value ""Constant
Year is highly overall correlated with Attendance_pctHigh correlation
Composite_class_count is highly overall correlated with Composite_class_studentsHigh correlation
Composite_class_students is highly overall correlated with Composite_class_countHigh correlation
Pct_composite_classes is highly overall correlated with Pct_composite_class_students and 1 other fieldsHigh correlation
Pct_composite_class_students is highly overall correlated with Pct_composite_classes and 1 other fieldsHigh correlation
Attendance_pct is highly overall correlated with Year and 1 other fieldsHigh correlation
ICSEA_value is highly overall correlated with Attendance_pctHigh correlation
latest_year_enrolment_FTE is highly overall correlated with Pct_composite_classes and 1 other fieldsHigh correlation
Latitude is highly overall correlated with Longitude and 1 other fieldsHigh correlation
Longitude is highly overall correlated with Latitude and 1 other fieldsHigh correlation
Level_of_schooling is highly overall correlated with School_subtypeHigh correlation
School_subtype is highly overall correlated with Level_of_schoolingHigh correlation
Late_opening_school is highly overall correlated with Longitude and 1 other fieldsHigh correlation
ASGS_remoteness is highly overall correlated with Late_opening_schoolHigh correlation
Operational_directorate is highly overall correlated with LatitudeHigh correlation
Level_of_schooling is highly imbalanced (80.7%)Imbalance
Selective_school is highly imbalanced (99.3%)Imbalance
Opportunity_class is highly imbalanced (73.1%)Imbalance
School_specialty_type is highly imbalanced (99.3%)Imbalance
School_subtype is highly imbalanced (80.7%)Imbalance
Preschool_ind is highly imbalanced (67.6%)Imbalance
Late_opening_school is highly imbalanced (68.1%)Imbalance
Attendance_pct has 200 (1.5%) missing valuesMissing
Composite_class_count has 610 (4.6%) zerosZeros
Composite_class_students has 610 (4.6%) zerosZeros
Pct_composite_classes has 515 (3.9%) zerosZeros
Pct_composite_class_students has 515 (3.9%) zerosZeros

Reproduction

Analysis started2023-08-20 01:38:32.059237
Analysis finished2023-08-20 01:38:51.039813
Duration18.98 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

School_code
Real number (ℝ)

Distinct1656
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3164.7065
Minimum1001
Maximum8819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.9 KiB
2023-08-20T01:38:51.166355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1203
Q12085.5
median3128
Q34206.25
95-th percentile4663
Maximum8819
Range7818
Interquartile range (IQR)2120.75

Descriptive statistics

Standard deviation1340.7386
Coefficient of variation (CV)0.42365336
Kurtosis0.82778542
Mean3164.7065
Median Absolute Deviation (MAD)1066.5
Skewness0.61267474
Sum41926032
Variance1797579.9
MonotonicityNot monotonic
2023-08-20T01:38:51.374219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001 8
 
0.1%
4258 8
 
0.1%
2550 8
 
0.1%
2528 8
 
0.1%
3846 8
 
0.1%
2506 8
 
0.1%
2502 8
 
0.1%
2489 8
 
0.1%
3952 8
 
0.1%
4282 8
 
0.1%
Other values (1646) 13168
99.4%
ValueCountFrequency (%)
1001 8
0.1%
1002 8
0.1%
1003 8
0.1%
1007 8
0.1%
1008 8
0.1%
1009 8
0.1%
1015 8
0.1%
1016 8
0.1%
1017 8
0.1%
1019 8
0.1%
ValueCountFrequency (%)
8819 8
0.1%
8556 8
0.1%
8278 8
0.1%
8271 8
0.1%
7445 8
0.1%
7444 8
0.1%
7442 8
0.1%
7436 8
0.1%
7435 8
0.1%
7432 8
0.1%
Distinct1656
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
2023-08-20T01:38:51.596271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length34
Mean length23.934179
Min length17

Characters and Unicode

Total characters317080
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAbbotsford Public School
2nd rowAberdeen Public School
3rd rowAbermain Public School
4th rowAdaminaby Public School
5th rowAdamstown Public School
ValueCountFrequency (%)
school 13240
29.2%
public 12600
27.8%
central 472
 
1.0%
north 432
 
1.0%
west 424
 
0.9%
park 376
 
0.8%
south 344
 
0.8%
east 312
 
0.7%
heights 264
 
0.6%
hill 240
 
0.5%
Other values (1515) 16608
36.7%
2023-08-20T01:38:51.977628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 36152
 
11.4%
l 35144
 
11.1%
32064
 
10.1%
c 27176
 
8.6%
i 18992
 
6.0%
h 16504
 
5.2%
u 16280
 
5.1%
b 14776
 
4.7%
S 14528
 
4.6%
P 13848
 
4.4%
Other values (44) 91616
28.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 239608
75.6%
Uppercase Letter 45352
 
14.3%
Space Separator 32064
 
10.1%
Dash Punctuation 48
 
< 0.1%
Other Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 36152
15.1%
l 35144
14.7%
c 27176
11.3%
i 18992
7.9%
h 16504
6.9%
u 16280
6.8%
b 14776
 
6.2%
a 13728
 
5.7%
e 11608
 
4.8%
r 10560
 
4.4%
Other values (16) 38688
16.1%
Uppercase Letter
ValueCountFrequency (%)
S 14528
32.0%
P 13848
30.5%
C 2232
 
4.9%
B 2040
 
4.5%
W 1608
 
3.5%
M 1328
 
2.9%
H 1312
 
2.9%
T 960
 
2.1%
G 952
 
2.1%
N 904
 
2.0%
Other values (15) 5640
 
12.4%
Space Separator
ValueCountFrequency (%)
32064
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%
Other Punctuation
ValueCountFrequency (%)
' 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 284960
89.9%
Common 32120
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 36152
12.7%
l 35144
12.3%
c 27176
 
9.5%
i 18992
 
6.7%
h 16504
 
5.8%
u 16280
 
5.7%
b 14776
 
5.2%
S 14528
 
5.1%
P 13848
 
4.9%
a 13728
 
4.8%
Other values (41) 77832
27.3%
Common
ValueCountFrequency (%)
32064
99.8%
- 48
 
0.1%
' 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 317080
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 36152
 
11.4%
l 35144
 
11.1%
32064
 
10.1%
c 27176
 
8.6%
i 18992
 
6.0%
h 16504
 
5.2%
u 16280
 
5.1%
b 14776
 
4.7%
S 14528
 
4.6%
P 13848
 
4.4%
Other values (44) 91616
28.9%

Year
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.75
Minimum2014
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.9 KiB
2023-08-20T01:38:52.107941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12015.75
median2017.5
Q32019.5
95-th percentile2022
Maximum2022
Range8
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation2.6340129
Coefficient of variation (CV)0.0013054208
Kurtosis-1.1692116
Mean2017.75
Median Absolute Deviation (MAD)2
Skewness0.23090241
Sum26731152
Variance6.9380237
MonotonicityNot monotonic
2023-08-20T01:38:52.233580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2014 1656
12.5%
2015 1656
12.5%
2016 1656
12.5%
2017 1656
12.5%
2018 1656
12.5%
2019 1656
12.5%
2021 1656
12.5%
2022 1656
12.5%
ValueCountFrequency (%)
2014 1656
12.5%
2015 1656
12.5%
2016 1656
12.5%
2017 1656
12.5%
2018 1656
12.5%
2019 1656
12.5%
2021 1656
12.5%
2022 1656
12.5%
ValueCountFrequency (%)
2022 1656
12.5%
2021 1656
12.5%
2019 1656
12.5%
2018 1656
12.5%
2017 1656
12.5%
2016 1656
12.5%
2015 1656
12.5%
2014 1656
12.5%

Composite_class_count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.218901
Minimum0
Maximum29
Zeros610
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size51.9 KiB
2023-08-20T01:38:52.357434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q36
95-th percentile10
Maximum29
Range29
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2302636
Coefficient of variation (CV)0.76566472
Kurtosis3.0900539
Mean4.218901
Median Absolute Deviation (MAD)2
Skewness1.4134012
Sum55892
Variance10.434603
MonotonicityNot monotonic
2023-08-20T01:38:52.517088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 2429
18.3%
3 2280
17.2%
1 1727
13.0%
4 1396
10.5%
5 1054
8.0%
6 989
7.5%
7 784
 
5.9%
0 610
 
4.6%
8 564
 
4.3%
9 460
 
3.5%
Other values (19) 955
 
7.2%
ValueCountFrequency (%)
0 610
 
4.6%
1 1727
13.0%
2 2429
18.3%
3 2280
17.2%
4 1396
10.5%
5 1054
8.0%
6 989
7.5%
7 784
 
5.9%
8 564
 
4.3%
9 460
 
3.5%
ValueCountFrequency (%)
29 2
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
26 1
 
< 0.1%
25 2
 
< 0.1%
24 1
 
< 0.1%
22 1
 
< 0.1%
21 4
< 0.1%
20 7
0.1%
19 7
0.1%

Composite_class_students
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct456
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.83605
Minimum0
Maximum779
Zeros610
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size51.9 KiB
2023-08-20T01:38:52.702480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q135
median79
Q3151.25
95-th percentile274
Maximum779
Range779
Interquartile range (IQR)116.25

Descriptive statistics

Standard deviation86.988907
Coefficient of variation (CV)0.84589895
Kurtosis2.7727443
Mean102.83605
Median Absolute Deviation (MAD)51
Skewness1.3829559
Sum1362372
Variance7567.07
MonotonicityNot monotonic
2023-08-20T01:38:52.911335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 610
 
4.6%
30 176
 
1.3%
29 154
 
1.2%
28 146
 
1.1%
53 141
 
1.1%
54 138
 
1.0%
24 133
 
1.0%
26 131
 
1.0%
25 130
 
1.0%
27 124
 
0.9%
Other values (446) 11365
85.8%
ValueCountFrequency (%)
0 610
4.6%
2 5
 
< 0.1%
3 5
 
< 0.1%
4 16
 
0.1%
5 28
 
0.2%
6 35
 
0.3%
7 47
 
0.4%
8 61
 
0.5%
9 68
 
0.5%
10 70
 
0.5%
ValueCountFrequency (%)
779 1
< 0.1%
724 1
< 0.1%
716 1
< 0.1%
689 1
< 0.1%
670 1
< 0.1%
660 1
< 0.1%
657 1
< 0.1%
656 1
< 0.1%
595 1
< 0.1%
559 1
< 0.1%

Pct_composite_classes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct293
Distinct (%)2.2%
Missing95
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean57.198495
Minimum0
Maximum100
Zeros515
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size51.9 KiB
2023-08-20T01:38:53.119562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.4000001
Q122.200001
median60
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)77.799999

Descriptive statistics

Standard deviation35.463226
Coefficient of variation (CV)0.62000279
Kurtosis-1.4449605
Mean57.198495
Median Absolute Deviation (MAD)40
Skewness-0.17456521
Sum752331.8
Variance1257.6405
MonotonicityNot monotonic
2023-08-20T01:38:53.321210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 3473
26.2%
0 515
 
3.9%
50 459
 
3.5%
75 436
 
3.3%
80 314
 
2.4%
66.69999695 291
 
2.2%
83.30000305 252
 
1.9%
60 247
 
1.9%
33.29999924 234
 
1.8%
85.69999695 210
 
1.6%
Other values (283) 6722
50.7%
ValueCountFrequency (%)
0 515
3.9%
1.299999952 1
 
< 0.1%
1.399999976 1
 
< 0.1%
1.700000048 1
 
< 0.1%
1.799999952 1
 
< 0.1%
1.899999976 5
 
< 0.1%
2 3
 
< 0.1%
2.099999905 3
 
< 0.1%
2.200000048 2
 
< 0.1%
2.299999952 6
 
< 0.1%
ValueCountFrequency (%)
100 3473
26.2%
92.90000153 1
 
< 0.1%
92.30000305 3
 
< 0.1%
91.69999695 3
 
< 0.1%
90.90000153 18
 
0.1%
90 52
 
0.4%
88.90000153 98
 
0.7%
88.19999695 3
 
< 0.1%
87.5 134
 
1.0%
86.69999695 24
 
0.2%

Pct_composite_class_students
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct926
Distinct (%)7.0%
Missing95
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean59.265035
Minimum0
Maximum100
Zeros515
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size51.9 KiB
2023-08-20T01:38:53.518085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.5999999
Q124
median64.400002
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)76

Descriptive statistics

Standard deviation35.494373
Coefficient of variation (CV)0.59890918
Kurtosis-1.4188178
Mean59.265035
Median Absolute Deviation (MAD)35.599998
Skewness-0.28578883
Sum779513
Variance1259.8506
MonotonicityNot monotonic
2023-08-20T01:38:53.721118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 3473
 
26.2%
0 515
 
3.9%
4.099999905 33
 
0.2%
83.5 30
 
0.2%
84.59999847 29
 
0.2%
66.69999695 28
 
0.2%
88.09999847 28
 
0.2%
86.90000153 28
 
0.2%
84.30000305 28
 
0.2%
87 27
 
0.2%
Other values (916) 8934
67.4%
(Missing) 95
 
0.7%
ValueCountFrequency (%)
0 515
3.9%
0.3000000119 1
 
< 0.1%
1.299999952 2
 
< 0.1%
1.700000048 1
 
< 0.1%
1.799999952 2
 
< 0.1%
1.899999976 1
 
< 0.1%
2 6
 
< 0.1%
2.099999905 4
 
< 0.1%
2.299999952 4
 
< 0.1%
2.400000095 2
 
< 0.1%
ValueCountFrequency (%)
100 3473
26.2%
99 1
 
< 0.1%
94.40000153 1
 
< 0.1%
94.19999695 1
 
< 0.1%
93.80000305 1
 
< 0.1%
93.69999695 1
 
< 0.1%
93.59999847 1
 
< 0.1%
93.5 4
 
< 0.1%
93.40000153 1
 
< 0.1%
93.19999695 1
 
< 0.1%

Attendance_pct
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct276
Distinct (%)2.1%
Missing200
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean91.940175
Minimum50.599998
Maximum99.199997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.9 KiB
2023-08-20T01:38:54.120839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50.599998
5-th percentile84.599998
Q190.599998
median92.900002
Q394.400002
95-th percentile96
Maximum99.199997
Range48.599998
Interquartile range (IQR)3.8000031

Descriptive statistics

Standard deviation3.8291414
Coefficient of variation (CV)0.041648185
Kurtosis9.639492
Mean91.940175
Median Absolute Deviation (MAD)1.7999954
Skewness-2.2210271
Sum1199635.4
Variance14.662324
MonotonicityNot monotonic
2023-08-20T01:38:54.317733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.09999847 249
 
1.9%
94.30000305 240
 
1.8%
94.19999695 237
 
1.8%
93.09999847 222
 
1.7%
93.90000153 220
 
1.7%
94.5 215
 
1.6%
94 214
 
1.6%
93.59999847 214
 
1.6%
94.59999847 213
 
1.6%
93.30000305 209
 
1.6%
Other values (266) 10815
81.6%
ValueCountFrequency (%)
50.59999847 1
< 0.1%
51.20000076 1
< 0.1%
51.40000153 1
< 0.1%
58.09999847 1
< 0.1%
59.20000076 1
< 0.1%
59.40000153 1
< 0.1%
61.59999847 1
< 0.1%
62.70000076 1
< 0.1%
63.70000076 1
< 0.1%
64.30000305 1
< 0.1%
ValueCountFrequency (%)
99.19999695 1
 
< 0.1%
98.80000305 2
 
< 0.1%
98.5 1
 
< 0.1%
98.30000305 1
 
< 0.1%
98.19999695 1
 
< 0.1%
98.09999847 3
< 0.1%
98 2
 
< 0.1%
97.90000153 4
< 0.1%
97.80000305 6
< 0.1%
97.69999695 5
< 0.1%

ICSEA_value
Real number (ℝ)

HIGH CORRELATION 

Distinct407
Distinct (%)3.1%
Missing64
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean982.6068
Minimum586
Maximum1186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size103.6 KiB
2023-08-20T01:38:54.507242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum586
5-th percentile826
Q1925
median980
Q31049
95-th percentile1141
Maximum1186
Range600
Interquartile range (IQR)124

Descriptive statistics

Standard deviation96.776322
Coefficient of variation (CV)0.098489368
Kurtosis0.77307676
Mean982.6068
Median Absolute Deviation (MAD)60
Skewness-0.42741727
Sum12954688
Variance9365.6565
MonotonicityNot monotonic
2023-08-20T01:38:54.698543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
985 120
 
0.9%
994 112
 
0.8%
973 88
 
0.7%
935 88
 
0.7%
940 88
 
0.7%
959 88
 
0.7%
917 88
 
0.7%
927 88
 
0.7%
978 88
 
0.7%
1009 88
 
0.7%
Other values (397) 12248
92.5%
ValueCountFrequency (%)
586 8
0.1%
595 8
0.1%
622 16
0.1%
631 8
0.1%
640 16
0.1%
646 8
0.1%
650 8
0.1%
652 8
0.1%
662 8
0.1%
667 8
0.1%
ValueCountFrequency (%)
1186 16
0.1%
1185 8
0.1%
1183 8
0.1%
1182 8
0.1%
1181 8
0.1%
1179 8
0.1%
1178 8
0.1%
1177 8
0.1%
1176 16
0.1%
1175 16
0.1%

Level_of_schooling
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
Primary School
12624 
Central/Community School
 
512
Infants School
 
112

Length

Max length24
Median length14
Mean length14.386473
Min length14

Characters and Unicode

Total characters190592
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrimary School
2nd rowPrimary School
3rd rowPrimary School
4th rowPrimary School
5th rowPrimary School

Common Values

ValueCountFrequency (%)
Primary School 12624
95.3%
Central/Community School 512
 
3.9%
Infants School 112
 
0.8%

Length

2023-08-20T01:38:54.880228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-20T01:38:55.022512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
school 13248
50.0%
primary 12624
47.6%
central/community 512
 
1.9%
infants 112
 
0.4%

Most occurring characters

ValueCountFrequency (%)
o 27008
14.2%
r 25760
13.5%
l 13760
7.2%
m 13648
7.2%
a 13248
7.0%
13248
7.0%
S 13248
7.0%
c 13248
7.0%
h 13248
7.0%
i 13136
6.9%
Other values (11) 31040
16.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 149824
78.6%
Uppercase Letter 27008
 
14.2%
Space Separator 13248
 
7.0%
Other Punctuation 512
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 27008
18.0%
r 25760
17.2%
l 13760
9.2%
m 13648
9.1%
a 13248
8.8%
c 13248
8.8%
h 13248
8.8%
i 13136
8.8%
y 13136
8.8%
n 1248
 
0.8%
Other values (5) 2384
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
S 13248
49.1%
P 12624
46.7%
C 1024
 
3.8%
I 112
 
0.4%
Space Separator
ValueCountFrequency (%)
13248
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 176832
92.8%
Common 13760
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 27008
15.3%
r 25760
14.6%
l 13760
7.8%
m 13648
7.7%
a 13248
7.5%
S 13248
7.5%
c 13248
7.5%
h 13248
7.5%
i 13136
7.4%
y 13136
7.4%
Other values (9) 17392
9.8%
Common
ValueCountFrequency (%)
13248
96.3%
/ 512
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 27008
14.2%
r 25760
13.5%
l 13760
7.2%
m 13648
7.2%
a 13248
7.0%
13248
7.0%
S 13248
7.0%
c 13248
7.0%
h 13248
7.0%
i 13136
6.9%
Other values (11) 31040
16.3%

latest_year_enrolment_FTE
Real number (ℝ)

HIGH CORRELATION 

Distinct684
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean292.54275
Minimum2
Maximum2079
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size103.6 KiB
2023-08-20T01:38:55.189296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12
Q184
median250
Q3433
95-th percentile757
Maximum2079
Range2077
Interquartile range (IQR)349

Descriptive statistics

Standard deviation250.21
Coefficient of variation (CV)0.85529379
Kurtosis3.5992992
Mean292.54275
Median Absolute Deviation (MAD)173
Skewness1.3778332
Sum3875606.4
Variance62605.044
MonotonicityNot monotonic
2023-08-20T01:38:55.376425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 136
 
1.0%
12 120
 
0.9%
10 104
 
0.8%
22 96
 
0.7%
16 96
 
0.7%
26 96
 
0.7%
14 96
 
0.7%
9 88
 
0.7%
23 80
 
0.6%
19 72
 
0.5%
Other values (674) 12264
92.6%
ValueCountFrequency (%)
2 16
 
0.1%
3 8
 
0.1%
4 32
 
0.2%
5 16
 
0.1%
6 48
 
0.4%
7 72
0.5%
8 64
0.5%
9 88
0.7%
10 104
0.8%
11 136
1.0%
ValueCountFrequency (%)
2079 8
0.1%
1874 8
0.1%
1637.4 8
0.1%
1452 8
0.1%
1365 8
0.1%
1305 8
0.1%
1260 8
0.1%
1237 8
0.1%
1229 8
0.1%
1150 8
0.1%
Distinct80
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
2023-08-20T01:38:55.576352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.1539855
Min length2

Characters and Unicode

Total characters41784
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row22.0
3rd row26.0
4th row0.0
5th row8.0
ValueCountFrequency (%)
np 3232
24.4%
0.0 872
 
6.6%
2.0 616
 
4.6%
4.0 480
 
3.6%
1.0 480
 
3.6%
3.0 472
 
3.6%
7.0 424
 
3.2%
6.0 384
 
2.9%
9.0 376
 
2.8%
5.0 360
 
2.7%
Other values (70) 5552
41.9%
2023-08-20T01:38:55.922234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11640
27.9%
. 10016
24.0%
1 3608
 
8.6%
n 3232
 
7.7%
p 3232
 
7.7%
2 2584
 
6.2%
3 1656
 
4.0%
4 1192
 
2.9%
5 1080
 
2.6%
6 1008
 
2.4%
Other values (3) 2536
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25304
60.6%
Other Punctuation 10016
 
24.0%
Lowercase Letter 6464
 
15.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11640
46.0%
1 3608
 
14.3%
2 2584
 
10.2%
3 1656
 
6.5%
4 1192
 
4.7%
5 1080
 
4.3%
6 1008
 
4.0%
7 896
 
3.5%
8 848
 
3.4%
9 792
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
n 3232
50.0%
p 3232
50.0%
Other Punctuation
ValueCountFrequency (%)
. 10016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 35320
84.5%
Latin 6464
 
15.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11640
33.0%
. 10016
28.4%
1 3608
 
10.2%
2 2584
 
7.3%
3 1656
 
4.7%
4 1192
 
3.4%
5 1080
 
3.1%
6 1008
 
2.9%
7 896
 
2.5%
8 848
 
2.4%
Latin
ValueCountFrequency (%)
n 3232
50.0%
p 3232
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41784
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11640
27.9%
. 10016
24.0%
1 3608
 
8.6%
n 3232
 
7.7%
p 3232
 
7.7%
2 2584
 
6.2%
3 1656
 
4.0%
4 1192
 
2.9%
5 1080
 
2.6%
6 1008
 
2.4%
Other values (3) 2536
 
6.1%
Distinct101
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
2023-08-20T01:38:56.173205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.3538647
Min length2

Characters and Unicode

Total characters44432
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41.0
2nd rownp
3rd rownp
4th rownp
5th row14.0
ValueCountFrequency (%)
np 2176
 
16.4%
0.0 1416
 
10.7%
6.0 520
 
3.9%
5.0 456
 
3.4%
7.0 416
 
3.1%
8.0 416
 
3.1%
9.0 384
 
2.9%
4.0 344
 
2.6%
10.0 336
 
2.5%
12.0 280
 
2.1%
Other values (91) 6504
49.1%
2023-08-20T01:38:56.605008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13424
30.2%
. 11072
24.9%
1 2704
 
6.1%
n 2176
 
4.9%
p 2176
 
4.9%
2 1800
 
4.1%
6 1680
 
3.8%
3 1656
 
3.7%
7 1576
 
3.5%
4 1568
 
3.5%
Other values (3) 4600
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29008
65.3%
Other Punctuation 11072
 
24.9%
Lowercase Letter 4352
 
9.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13424
46.3%
1 2704
 
9.3%
2 1800
 
6.2%
6 1680
 
5.8%
3 1656
 
5.7%
7 1576
 
5.4%
4 1568
 
5.4%
5 1560
 
5.4%
9 1552
 
5.4%
8 1488
 
5.1%
Lowercase Letter
ValueCountFrequency (%)
n 2176
50.0%
p 2176
50.0%
Other Punctuation
ValueCountFrequency (%)
. 11072
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40080
90.2%
Latin 4352
 
9.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13424
33.5%
. 11072
27.6%
1 2704
 
6.7%
2 1800
 
4.5%
6 1680
 
4.2%
3 1656
 
4.1%
7 1576
 
3.9%
4 1568
 
3.9%
5 1560
 
3.9%
9 1552
 
3.9%
Latin
ValueCountFrequency (%)
n 2176
50.0%
p 2176
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44432
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13424
30.2%
. 11072
24.9%
1 2704
 
6.1%
n 2176
 
4.9%
p 2176
 
4.9%
2 1800
 
4.1%
6 1680
 
3.8%
3 1656
 
3.7%
7 1576
 
3.5%
4 1568
 
3.5%
Other values (3) 4600
 
10.4%

Selective_school
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
Not Selective
13240 
Partially Selective
 
8

Length

Max length19
Median length13
Mean length13.003623
Min length13

Characters and Unicode

Total characters172272
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Selective
2nd rowNot Selective
3rd rowNot Selective
4th rowNot Selective
5th rowNot Selective

Common Values

ValueCountFrequency (%)
Not Selective 13240
99.9%
Partially Selective 8
 
0.1%

Length

2023-08-20T01:38:56.808937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-20T01:38:56.949244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
selective 13248
50.0%
not 13240
50.0%
partially 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 39744
23.1%
t 26496
15.4%
l 13264
 
7.7%
i 13256
 
7.7%
13248
 
7.7%
S 13248
 
7.7%
c 13248
 
7.7%
v 13248
 
7.7%
N 13240
 
7.7%
o 13240
 
7.7%
Other values (4) 40
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 132528
76.9%
Uppercase Letter 26496
 
15.4%
Space Separator 13248
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 39744
30.0%
t 26496
20.0%
l 13264
 
10.0%
i 13256
 
10.0%
c 13248
 
10.0%
v 13248
 
10.0%
o 13240
 
10.0%
a 16
 
< 0.1%
r 8
 
< 0.1%
y 8
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
S 13248
50.0%
N 13240
50.0%
P 8
 
< 0.1%
Space Separator
ValueCountFrequency (%)
13248
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 159024
92.3%
Common 13248
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 39744
25.0%
t 26496
16.7%
l 13264
 
8.3%
i 13256
 
8.3%
S 13248
 
8.3%
c 13248
 
8.3%
v 13248
 
8.3%
N 13240
 
8.3%
o 13240
 
8.3%
a 16
 
< 0.1%
Other values (3) 24
 
< 0.1%
Common
ValueCountFrequency (%)
13248
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 172272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 39744
23.1%
t 26496
15.4%
l 13264
 
7.7%
i 13256
 
7.7%
13248
 
7.7%
S 13248
 
7.7%
c 13248
 
7.7%
v 13248
 
7.7%
N 13240
 
7.7%
o 13240
 
7.7%
Other values (4) 40
 
< 0.1%

Opportunity_class
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
False
12640 
True
 
608
ValueCountFrequency (%)
False 12640
95.4%
True 608
 
4.6%
2023-08-20T01:38:57.068851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

School_specialty_type
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
Comprehensive
13240 
Performing Arts
 
8

Length

Max length15
Median length13
Mean length13.001208
Min length13

Characters and Unicode

Total characters172240
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowComprehensive
2nd rowComprehensive
3rd rowComprehensive
4th rowComprehensive
5th rowComprehensive

Common Values

ValueCountFrequency (%)
Comprehensive 13240
99.9%
Performing Arts 8
 
0.1%

Length

2023-08-20T01:38:57.252299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-20T01:38:57.511033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
comprehensive 13240
99.9%
performing 8
 
0.1%
arts 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 39728
23.1%
r 13264
 
7.7%
s 13248
 
7.7%
o 13248
 
7.7%
m 13248
 
7.7%
i 13248
 
7.7%
n 13248
 
7.7%
v 13240
 
7.7%
C 13240
 
7.7%
h 13240
 
7.7%
Other values (7) 13288
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 158976
92.3%
Uppercase Letter 13256
 
7.7%
Space Separator 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 39728
25.0%
r 13264
 
8.3%
s 13248
 
8.3%
o 13248
 
8.3%
m 13248
 
8.3%
i 13248
 
8.3%
n 13248
 
8.3%
v 13240
 
8.3%
h 13240
 
8.3%
p 13240
 
8.3%
Other values (3) 24
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
C 13240
99.9%
P 8
 
0.1%
A 8
 
0.1%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 172232
> 99.9%
Common 8
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 39728
23.1%
r 13264
 
7.7%
s 13248
 
7.7%
o 13248
 
7.7%
m 13248
 
7.7%
i 13248
 
7.7%
n 13248
 
7.7%
v 13240
 
7.7%
C 13240
 
7.7%
h 13240
 
7.7%
Other values (6) 13280
 
7.7%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 172240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 39728
23.1%
r 13264
 
7.7%
s 13248
 
7.7%
o 13248
 
7.7%
m 13248
 
7.7%
i 13248
 
7.7%
n 13248
 
7.7%
v 13240
 
7.7%
C 13240
 
7.7%
h 13240
 
7.7%
Other values (7) 13288
 
7.7%

School_subtype
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
Kinder to Year 6
12624 
Kinder to Year 12
 
512
Kinder to Year 2
 
112

Length

Max length17
Median length16
Mean length16.038647
Min length16

Characters and Unicode

Total characters212480
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKinder to Year 6
2nd rowKinder to Year 6
3rd rowKinder to Year 6
4th rowKinder to Year 6
5th rowKinder to Year 6

Common Values

ValueCountFrequency (%)
Kinder to Year 6 12624
95.3%
Kinder to Year 12 512
 
3.9%
Kinder to Year 2 112
 
0.8%

Length

2023-08-20T01:38:57.697446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-20T01:38:57.864450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kinder 13248
25.0%
to 13248
25.0%
year 13248
25.0%
6 12624
23.8%
12 512
 
1.0%
2 112
 
0.2%

Most occurring characters

ValueCountFrequency (%)
39744
18.7%
e 26496
12.5%
r 26496
12.5%
K 13248
 
6.2%
i 13248
 
6.2%
n 13248
 
6.2%
d 13248
 
6.2%
t 13248
 
6.2%
o 13248
 
6.2%
Y 13248
 
6.2%
Other values (4) 27008
12.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 132480
62.3%
Space Separator 39744
 
18.7%
Uppercase Letter 26496
 
12.5%
Decimal Number 13760
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 26496
20.0%
r 26496
20.0%
i 13248
10.0%
n 13248
10.0%
d 13248
10.0%
t 13248
10.0%
o 13248
10.0%
a 13248
10.0%
Decimal Number
ValueCountFrequency (%)
6 12624
91.7%
2 624
 
4.5%
1 512
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
K 13248
50.0%
Y 13248
50.0%
Space Separator
ValueCountFrequency (%)
39744
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 158976
74.8%
Common 53504
 
25.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 26496
16.7%
r 26496
16.7%
K 13248
8.3%
i 13248
8.3%
n 13248
8.3%
d 13248
8.3%
t 13248
8.3%
o 13248
8.3%
Y 13248
8.3%
a 13248
8.3%
Common
ValueCountFrequency (%)
39744
74.3%
6 12624
 
23.6%
2 624
 
1.2%
1 512
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 212480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
39744
18.7%
e 26496
12.5%
r 26496
12.5%
K 13248
 
6.2%
i 13248
 
6.2%
n 13248
 
6.2%
d 13248
 
6.2%
t 13248
 
6.2%
o 13248
 
6.2%
Y 13248
 
6.2%
Other values (4) 27008
12.7%

Preschool_ind
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
False
12464 
True
 
784
ValueCountFrequency (%)
False 12464
94.1%
True 784
 
5.9%
2023-08-20T01:38:58.011384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Distance_education
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
False
13248 
ValueCountFrequency (%)
False 13248
100.0%
2023-08-20T01:38:58.131804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Intensive_english_centre
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
False
13248 
ValueCountFrequency (%)
False 13248
100.0%
2023-08-20T01:38:58.250521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

School_gender
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
Coed
13248 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters52992
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCoed
2nd rowCoed
3rd rowCoed
4th rowCoed
5th rowCoed

Common Values

ValueCountFrequency (%)
Coed 13248
100.0%

Length

2023-08-20T01:38:58.395107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-20T01:38:58.532995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
coed 13248
100.0%

Most occurring characters

ValueCountFrequency (%)
C 13248
25.0%
o 13248
25.0%
e 13248
25.0%
d 13248
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39744
75.0%
Uppercase Letter 13248
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 13248
33.3%
e 13248
33.3%
d 13248
33.3%
Uppercase Letter
ValueCountFrequency (%)
C 13248
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 52992
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 13248
25.0%
o 13248
25.0%
e 13248
25.0%
d 13248
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 13248
25.0%
o 13248
25.0%
e 13248
25.0%
d 13248
25.0%

Late_opening_school
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
False
12480 
True
 
768
ValueCountFrequency (%)
False 12480
94.2%
True 768
 
5.8%
2023-08-20T01:38:58.647296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

ASGS_remoteness
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
Major Cities of Australia
6896 
Inner Regional Australia
3680 
Outer Regional Australia
2320 
Remote Australia
 
256
Very Remote Australia
 
96

Length

Max length25
Median length25
Mean length24.344203
Min length16

Characters and Unicode

Total characters322512
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMajor Cities of Australia
2nd rowInner Regional Australia
3rd rowMajor Cities of Australia
4th rowOuter Regional Australia
5th rowMajor Cities of Australia

Common Values

ValueCountFrequency (%)
Major Cities of Australia 6896
52.1%
Inner Regional Australia 3680
27.8%
Outer Regional Australia 2320
 
17.5%
Remote Australia 256
 
1.9%
Very Remote Australia 96
 
0.7%

Length

2023-08-20T01:38:58.793617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-20T01:38:58.946047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
australia 13248
28.6%
major 6896
14.9%
cities 6896
14.9%
of 6896
14.9%
regional 6000
12.9%
inner 3680
 
7.9%
outer 2320
 
5.0%
remote 352
 
0.8%
very 96
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a 39392
12.2%
33136
10.3%
i 33040
10.2%
r 26240
 
8.1%
t 22816
 
7.1%
o 20144
 
6.2%
s 20144
 
6.2%
e 19696
 
6.1%
l 19248
 
6.0%
u 15568
 
4.8%
Other values (13) 73088
22.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 249888
77.5%
Uppercase Letter 39488
 
12.2%
Space Separator 33136
 
10.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 39392
15.8%
i 33040
13.2%
r 26240
10.5%
t 22816
9.1%
o 20144
8.1%
s 20144
8.1%
e 19696
7.9%
l 19248
7.7%
u 15568
 
6.2%
n 13360
 
5.3%
Other values (5) 20240
8.1%
Uppercase Letter
ValueCountFrequency (%)
A 13248
33.5%
M 6896
17.5%
C 6896
17.5%
R 6352
16.1%
I 3680
 
9.3%
O 2320
 
5.9%
V 96
 
0.2%
Space Separator
ValueCountFrequency (%)
33136
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 289376
89.7%
Common 33136
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 39392
13.6%
i 33040
11.4%
r 26240
9.1%
t 22816
 
7.9%
o 20144
 
7.0%
s 20144
 
7.0%
e 19696
 
6.8%
l 19248
 
6.7%
u 15568
 
5.4%
n 13360
 
4.6%
Other values (12) 59728
20.6%
Common
ValueCountFrequency (%)
33136
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 322512
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 39392
12.2%
33136
10.3%
i 33040
10.2%
r 26240
 
8.1%
t 22816
 
7.1%
o 20144
 
6.2%
s 20144
 
6.2%
e 19696
 
6.1%
l 19248
 
6.0%
u 15568
 
4.8%
Other values (13) 73088
22.7%

Latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct1655
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-33.112735
Minimum-37.084209
Maximum-28.16951
Zeros0
Zeros (%)0.0%
Negative13248
Negative (%)100.0%
Memory size103.6 KiB
2023-08-20T01:38:59.139364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-37.084209
5-th percentile-35.38609
Q1-33.985579
median-33.749179
Q3-32.723733
95-th percentile-28.85478
Maximum-28.16951
Range8.914699
Interquartile range (IQR)1.261846

Descriptive statistics

Standard deviation1.7964496
Coefficient of variation (CV)-0.054252528
Kurtosis0.75791786
Mean-33.112735
Median Absolute Deviation (MAD)0.6254155
Skewness1.0817359
Sum-438677.51
Variance3.227231
MonotonicityNot monotonic
2023-08-20T01:38:59.341461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-32.908099 16
 
0.1%
-33.852728 8
 
0.1%
-33.972901 8
 
0.1%
-32.696789 8
 
0.1%
-32.145113 8
 
0.1%
-33.814849 8
 
0.1%
-32.89536 8
 
0.1%
-33.955064 8
 
0.1%
-33.696425 8
 
0.1%
-33.906705 8
 
0.1%
Other values (1645) 13160
99.3%
ValueCountFrequency (%)
-37.084209 8
0.1%
-37.063271 8
0.1%
-37.041739 8
0.1%
-36.92962 8
0.1%
-36.925482 8
0.1%
-36.918986 8
0.1%
-36.886525 8
0.1%
-36.832664 8
0.1%
-36.768392 8
0.1%
-36.732802 8
0.1%
ValueCountFrequency (%)
-28.16951 8
0.1%
-28.195346 8
0.1%
-28.196814 8
0.1%
-28.215705 8
0.1%
-28.222422 8
0.1%
-28.223742 8
0.1%
-28.229959 8
0.1%
-28.240248 8
0.1%
-28.259375 8
0.1%
-28.262592 8
0.1%

Longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct1656
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.6051
Minimum141.43997
Maximum159.06903
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size103.6 KiB
2023-08-20T01:38:59.529804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum141.43997
5-th percentile146.71475
Q1150.51462
median150.99293
Q3151.37684
95-th percentile153.20498
Maximum159.06903
Range17.629062
Interquartile range (IQR)0.8622215

Descriptive statistics

Standard deviation1.9075283
Coefficient of variation (CV)0.012665762
Kurtosis4.7057884
Mean150.6051
Median Absolute Deviation (MAD)0.4261885
Skewness-1.6945753
Sum1995216.4
Variance3.6386642
MonotonicityNot monotonic
2023-08-20T01:38:59.730449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151.131206 8
 
0.1%
150.926373 8
 
0.1%
151.005819 8
 
0.1%
150.352867 8
 
0.1%
151.071939 8
 
0.1%
151.748666 8
 
0.1%
151.229111 8
 
0.1%
150.833642 8
 
0.1%
151.162658 8
 
0.1%
150.896425 8
 
0.1%
Other values (1646) 13168
99.4%
ValueCountFrequency (%)
141.43997 8
0.1%
141.441054 8
0.1%
141.456502 8
0.1%
141.460807 8
0.1%
141.462443 8
0.1%
141.471518 8
0.1%
141.89493 8
0.1%
141.918751 8
0.1%
142.010166 8
0.1%
142.012741 8
0.1%
ValueCountFrequency (%)
159.069032 8
0.1%
153.614826 8
0.1%
153.590695 8
0.1%
153.585664 8
0.1%
153.579497 8
0.1%
153.572189 8
0.1%
153.567798 8
0.1%
153.565122 8
0.1%
153.563707 8
0.1%
153.555416 8
0.1%

Operational_directorate
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
Rural South and West
1896 
Regional North
1864 
Rural North
1808 
Regional North and West
1608 
Regional South
1560 
Other values (4)
4512 

Length

Max length27
Median length21
Mean length17.907609
Min length11

Characters and Unicode

Total characters237240
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMetropolitan South
2nd rowRegional North and West
3rd rowRegional North
4th rowRural South and West
5th rowRegional North

Common Values

ValueCountFrequency (%)
Rural South and West 1896
14.3%
Regional North 1864
14.1%
Rural North 1808
13.6%
Regional North and West 1608
12.1%
Regional South 1560
11.8%
Metropolitan South 1512
11.4%
Metropolitan South and West 1424
10.7%
Metropolitan North 1416
10.7%
Connected Communities 160
 
1.2%

Length

2023-08-20T01:38:59.929763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-20T01:39:00.112792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
north 6696
18.4%
south 6392
17.6%
regional 5032
13.8%
and 4928
13.6%
west 4928
13.6%
metropolitan 4352
12.0%
rural 3704
10.2%
connected 160
 
0.4%
communities 160
 
0.4%

Most occurring characters

ValueCountFrequency (%)
o 27144
11.4%
t 27040
11.4%
23104
 
9.7%
a 18016
 
7.6%
n 14792
 
6.2%
e 14792
 
6.2%
r 14752
 
6.2%
l 13088
 
5.5%
h 13088
 
5.5%
u 10256
 
4.3%
Other values (13) 61168
25.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 182712
77.0%
Uppercase Letter 31424
 
13.2%
Space Separator 23104
 
9.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 27144
14.9%
t 27040
14.8%
a 18016
9.9%
n 14792
8.1%
e 14792
8.1%
r 14752
8.1%
l 13088
7.2%
h 13088
7.2%
u 10256
 
5.6%
i 9704
 
5.3%
Other values (6) 20040
11.0%
Uppercase Letter
ValueCountFrequency (%)
R 8736
27.8%
N 6696
21.3%
S 6392
20.3%
W 4928
15.7%
M 4352
13.8%
C 320
 
1.0%
Space Separator
ValueCountFrequency (%)
23104
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 214136
90.3%
Common 23104
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 27144
12.7%
t 27040
12.6%
a 18016
 
8.4%
n 14792
 
6.9%
e 14792
 
6.9%
r 14752
 
6.9%
l 13088
 
6.1%
h 13088
 
6.1%
u 10256
 
4.8%
i 9704
 
4.5%
Other values (12) 51464
24.0%
Common
ValueCountFrequency (%)
23104
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 237240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 27144
11.4%
t 27040
11.4%
23104
 
9.7%
a 18016
 
7.6%
n 14792
 
6.2%
e 14792
 
6.2%
r 14752
 
6.2%
l 13088
 
5.5%
h 13088
 
5.5%
u 10256
 
4.3%
Other values (13) 61168
25.8%
Distinct112
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
2023-08-20T01:39:00.422673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length16
Mean length10.39372
Min length4

Characters and Unicode

Total characters137696
Distinct characters52
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIron Cove
2nd rowUpper Hunter
3rd rowCessnock
4th rowEden-Monaro
5th rowGlenrock
ValueCountFrequency (%)
coast 624
 
3.1%
lake 624
 
3.1%
north 464
 
2.3%
the 440
 
2.2%
west 400
 
2.0%
macquarie 392
 
2.0%
port 328
 
1.7%
valley 280
 
1.4%
hunter 264
 
1.3%
hills 248
 
1.2%
Other values (125) 15784
79.5%
2023-08-20T01:39:00.898777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 13600
 
9.9%
e 12792
 
9.3%
r 10664
 
7.7%
o 9304
 
6.8%
n 8960
 
6.5%
l 7480
 
5.4%
t 7128
 
5.2%
6600
 
4.8%
i 6184
 
4.5%
s 5432
 
3.9%
Other values (42) 49552
36.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 110816
80.5%
Uppercase Letter 19904
 
14.5%
Space Separator 6600
 
4.8%
Dash Punctuation 216
 
0.2%
Decimal Number 160
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13600
12.3%
e 12792
11.5%
r 10664
9.6%
o 9304
 
8.4%
n 8960
 
8.1%
l 7480
 
6.7%
t 7128
 
6.4%
i 6184
 
5.6%
s 5432
 
4.9%
u 4440
 
4.0%
Other values (14) 24832
22.4%
Uppercase Letter
ValueCountFrequency (%)
C 2824
14.2%
M 2064
10.4%
W 1872
9.4%
B 1536
 
7.7%
H 1368
 
6.9%
L 1368
 
6.9%
T 1224
 
6.1%
G 1152
 
5.8%
P 1088
 
5.5%
N 1048
 
5.3%
Other values (14) 4360
21.9%
Decimal Number
ValueCountFrequency (%)
3 88
55.0%
2 72
45.0%
Space Separator
ValueCountFrequency (%)
6600
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 216
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 130720
94.9%
Common 6976
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13600
 
10.4%
e 12792
 
9.8%
r 10664
 
8.2%
o 9304
 
7.1%
n 8960
 
6.9%
l 7480
 
5.7%
t 7128
 
5.5%
i 6184
 
4.7%
s 5432
 
4.2%
u 4440
 
3.4%
Other values (38) 44736
34.2%
Common
ValueCountFrequency (%)
6600
94.6%
- 216
 
3.1%
3 88
 
1.3%
2 72
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 137696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 13600
 
9.9%
e 12792
 
9.3%
r 10664
 
7.7%
o 9304
 
6.8%
n 8960
 
6.5%
l 7480
 
5.4%
t 7128
 
5.2%
6600
 
4.8%
i 6184
 
4.5%
s 5432
 
3.9%
Other values (42) 49552
36.0%
Distinct1656
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
2023-08-20T01:39:01.187545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length47
Mean length37.453502
Min length27

Characters and Unicode

Total characters496184
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttps://abbotsford-p.schools.nsw.gov.au
2nd rowhttps://aberdeen-p.schools.nsw.gov.au
3rd rowhttps://abermain-p.schools.nsw.gov.au
4th rowhttps://adaminaby-p.schools.nsw.gov.au
5th rowhttps://adamstown-p.schools.nsw.gov.au
ValueCountFrequency (%)
https://abbotsford-p.schools.nsw.gov.au 8
 
0.1%
https://aldavilla-p.schools.nsw.gov.au 8
 
0.1%
https://adaminaby-p.schools.nsw.gov.au 8
 
0.1%
https://adamstown-p.schools.nsw.gov.au 8
 
0.1%
https://adelong-p.schools.nsw.gov.au 8
 
0.1%
https://albertpk-p.schools.nsw.gov.au 8
 
0.1%
https://albionpk-p.schools.nsw.gov.au 8
 
0.1%
https://albionpkr-p.schools.nsw.gov.au 8
 
0.1%
https://alburynth-p.schools.nsw.gov.au 8
 
0.1%
https://albury-p.schools.nsw.gov.au 8
 
0.1%
Other values (1646) 13168
99.4%
2023-08-20T01:39:01.636606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 57744
 
11.6%
. 52984
 
10.7%
o 48120
 
9.7%
t 32648
 
6.6%
h 30264
 
6.1%
p 27768
 
5.6%
/ 26632
 
5.4%
a 25616
 
5.2%
l 22048
 
4.4%
n 22024
 
4.4%
Other values (21) 150336
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 390104
78.6%
Other Punctuation 92864
 
18.7%
Dash Punctuation 13208
 
2.7%
Uppercase Letter 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 57744
14.8%
o 48120
12.3%
t 32648
 
8.4%
h 30264
 
7.8%
p 27768
 
7.1%
a 25616
 
6.6%
l 22048
 
5.7%
n 22024
 
5.6%
g 16984
 
4.4%
c 16752
 
4.3%
Other values (16) 90136
23.1%
Other Punctuation
ValueCountFrequency (%)
. 52984
57.1%
/ 26632
28.7%
: 13248
 
14.3%
Dash Punctuation
ValueCountFrequency (%)
- 13208
100.0%
Uppercase Letter
ValueCountFrequency (%)
J 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 390112
78.6%
Common 106072
 
21.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 57744
14.8%
o 48120
12.3%
t 32648
 
8.4%
h 30264
 
7.8%
p 27768
 
7.1%
a 25616
 
6.6%
l 22048
 
5.7%
n 22024
 
5.6%
g 16984
 
4.4%
c 16752
 
4.3%
Other values (17) 90144
23.1%
Common
ValueCountFrequency (%)
. 52984
50.0%
/ 26632
25.1%
: 13248
 
12.5%
- 13208
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 496184
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 57744
 
11.6%
. 52984
 
10.7%
o 48120
 
9.7%
t 32648
 
6.6%
h 30264
 
6.1%
p 27768
 
5.6%
/ 26632
 
5.4%
a 25616
 
5.2%
l 22048
 
4.4%
n 22024
 
4.4%
Other values (21) 150336
30.3%
Distinct717
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
Minimum1849-01-01 00:00:00
Maximum2022-01-28 00:00:00
2023-08-20T01:39:01.791039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:39:01.930049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-08-20T01:38:48.553513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:33.659408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:35.091197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:36.631397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:38.287229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:39.913205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:41.370783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:42.841716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:44.371827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:45.810161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:47.093377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:48.707567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:33.776166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:35.244872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:36.776204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:38.435922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:40.058507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:41.520317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:42.983773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:44.530685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:45.916472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:47.237341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:48.853923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:33.889795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:35.392081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:37.103098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:38.590714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:40.201303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:41.663919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:43.128315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:44.783647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:46.024248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:47.382116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:48.995799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:33.996155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:35.533418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:37.235633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:38.793106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:40.335659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:41.796660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:43.259664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:44.908135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:46.125095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:47.517307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:49.131529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:34.114864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:35.682058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:37.379069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:38.943861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:40.476239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:41.941306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:43.403887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:45.015055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:46.233258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:47.656189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:49.229289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:34.263873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:35.817813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:37.507722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:39.080680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:40.602715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:42.069347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:43.531731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:45.117705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:46.331481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:47.783822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:49.326148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:34.398888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:35.952142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:37.637869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:39.216725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:40.728896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:42.198304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:43.658794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:45.255478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:46.425593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:47.908586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:49.454400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:34.536516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:36.089233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:37.767454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:39.357461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:40.855341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:42.327914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:43.785225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:45.388087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:46.520685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:48.033355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:49.595663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:34.683688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:36.228988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:37.903298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:39.500166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:40.990155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:42.460844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:43.917796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:45.490855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:46.799242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:48.164334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:49.729332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:34.813597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:36.361248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:38.026706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:39.629862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:41.112263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:42.583481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:44.044526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:45.595820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:46.889642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:48.283724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:49.863573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:34.948299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:36.490709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:38.151099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:39.763114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:41.237362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:42.706346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:44.174589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:45.708474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:46.983917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:38:48.409311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-20T01:39:02.042606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
School_codeYearComposite_class_countComposite_class_studentsPct_composite_classesPct_composite_class_studentsAttendance_pctICSEA_valuelatest_year_enrolment_FTELatitudeLongitudeLevel_of_schoolingSelective_schoolOpportunity_classSchool_specialty_typeSchool_subtypePreschool_indLate_opening_schoolASGS_remotenessOperational_directorate
School_code1.0000.0000.0960.141-0.306-0.3080.0460.1190.337-0.1160.0250.4860.4990.0710.4990.4860.1120.1040.2060.176
Year0.0001.0000.0240.0050.0070.008-0.5510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Composite_class_count0.0960.0241.0000.9770.1290.125-0.081-0.0310.251-0.1040.0410.1120.0000.1050.0200.1120.1110.1030.1440.100
Composite_class_students0.1410.0050.9771.000-0.015-0.015-0.0170.0590.374-0.1360.0720.0910.0250.1240.0000.0910.1110.1420.1750.112
Pct_composite_classes-0.3060.0070.129-0.0151.0000.998-0.214-0.391-0.8100.164-0.1170.1060.0350.1710.0490.1060.1350.2030.3020.220
Pct_composite_class_students-0.3080.0080.125-0.0150.9981.000-0.213-0.388-0.8100.166-0.1160.1030.0460.1720.0510.1030.1290.1940.2940.218
Attendance_pct0.046-0.551-0.081-0.017-0.214-0.2131.0000.5070.195-0.1760.0330.1990.0000.0680.0000.1990.1270.2260.1780.176
ICSEA_value0.1190.000-0.0310.059-0.391-0.3880.5071.0000.417-0.2330.2160.1680.0440.1310.0600.1680.1570.3800.3450.319
latest_year_enrolment_FTE0.3370.0000.2510.374-0.810-0.8100.1950.4171.000-0.1890.1290.1420.3000.3100.3000.1420.0900.1990.2860.224
Latitude-0.1160.000-0.104-0.1360.1640.166-0.176-0.233-0.1891.0000.5640.1240.0090.0860.0490.1240.0980.3030.4230.551
Longitude0.0250.0000.0410.072-0.117-0.1160.0330.2160.1290.5641.0000.1880.0000.0850.0000.1880.1030.6190.4620.429
Level_of_schooling0.4860.0000.1120.0910.1060.1030.1990.1680.1420.1240.1881.0000.1220.0340.1221.0000.0880.2720.2420.191
Selective_school0.4990.0000.0000.0250.0350.0460.0000.0440.3000.0090.0000.1221.0000.1040.0000.1220.0000.0000.0160.064
Opportunity_class0.0710.0000.1050.1240.1710.1720.0680.1310.3100.0860.0850.0340.1041.0000.0000.0340.0000.0280.1080.117
School_specialty_type0.4990.0000.0200.0000.0490.0510.0000.0600.3000.0490.0000.1220.0000.0001.0000.1220.0000.0000.0160.056
School_subtype0.4860.0000.1120.0910.1060.1030.1990.1680.1420.1240.1881.0000.1220.0340.1221.0000.0880.2720.2420.191
Preschool_ind0.1120.0000.1110.1110.1350.1290.1270.1570.0900.0980.1030.0880.0000.0000.0000.0881.0000.0350.1530.203
Late_opening_school0.1040.0000.1030.1420.2030.1940.2260.3800.1990.3030.6190.2720.0000.0280.0000.2720.0351.0000.7060.412
ASGS_remoteness0.2060.0000.1440.1750.3020.2940.1780.3450.2860.4230.4620.2420.0160.1080.0160.2420.1530.7061.0000.451
Operational_directorate0.1760.0000.1000.1120.2200.2180.1760.3190.2240.5510.4290.1910.0640.1170.0560.1910.2030.4120.4511.000

Missing values

2023-08-20T01:38:50.115593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-20T01:38:50.609169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-20T01:38:50.921722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

School_codeSchool_nameYearComposite_class_countComposite_class_studentsPct_composite_classesPct_composite_class_studentsAttendance_pctICSEA_valueLevel_of_schoolinglatest_year_enrolment_FTEIndigenous_pctLBOTE_pctSelective_schoolOpportunity_classSchool_specialty_typeSchool_subtypePreschool_indDistance_educationIntensive_english_centreSchool_genderLate_opening_schoolASGS_remotenessLatitudeLongitudeOperational_directoratePrincipal_networkWebsiteDate_1st_teacher
01001Abbotsford Public School20141130250.00000053.50000095.3000031111.0Primary School474.02.041.0Not SelectiveNComprehensiveKinder to Year 6NNNCoedNMajor Cities of Australia-33.852728151.131206Metropolitan SouthIron Covehttps://abbotsford-p.schools.nsw.gov.au1925-04-01
11002Aberdeen Public School201412912.50000014.50000095.300003895.0Primary School166.022.0npNot SelectiveNComprehensiveKinder to Year 6NNNCoedNInner Regional Australia-32.166098150.888095Regional North and WestUpper Hunterhttps://aberdeen-p.schools.nsw.gov.au1864-02-01
21003Abermain Public School2014818988.90000290.90000292.800003893.0Primary School265.026.0npNot SelectiveNComprehensiveKinder to Year 6NNNCoedNMajor Cities of Australia-32.808920151.426499Regional NorthCessnockhttps://abermain-p.schools.nsw.gov.au1905-08-01
31007Adaminaby Public School2014119100.000000100.00000096.199997976.0Primary School12.00.0npNot SelectiveNComprehensiveKinder to Year 6NNNCoedNOuter Regional Australia-35.993292148.776721Rural South and WestEden-Monarohttps://adaminaby-p.schools.nsw.gov.au1869-01-01
41008Adamstown Public School2014514855.59999864.09999895.8000031060.0Primary School361.08.014.0Not SelectiveNComprehensiveKinder to Year 6NNNCoedNMajor Cities of Australia-32.932213151.730971Regional NorthGlenrockhttps://adamstown-p.schools.nsw.gov.au1877-07-01
51009Adelong Public School2014481100.000000100.00000093.400002962.0Primary School47.0npnpNot SelectiveNComprehensiveKinder to Year 6NNNCoedNInner Regional Australia-35.312333148.062802Rural South and WestGundagaihttps://adelong-p.schools.nsw.gov.au1860-03-01
64177Albert Park Public School2014355100.000000100.00000088.300003941.0Primary School92.029.09.0Not SelectiveNComprehensiveKinder to Year 6NNNCoedNInner Regional Australia-28.821563153.272498Rural NorthLismorehttps://albertpk-p.schools.nsw.gov.au1942-01-01
71015Albion Park Public School201438218.79999920.29999993.900002968.0Primary School467.09.07.0Not SelectiveNComprehensiveKinder to Year 6NNNCoedNMajor Cities of Australia-34.570257150.772620Regional SouthLake Illawarra Southhttps://albionpk-p.schools.nsw.gov.au1872-07-01
84121Albion Park Rail Public School20141237.7000008.50000093.699997897.0Primary School350.024.010.0Not SelectiveNComprehensiveKinder to Year 6NNNCoedNMajor Cities of Australia-34.567550150.798668Regional SouthLake Illawarra Southhttps://albionpkr-p.schools.nsw.gov.au1959-01-01
93922Albury North Public School2014615654.50000059.79999993.400002888.0Primary School256.020.014.0Not SelectiveNComprehensiveKinder to Year 6NNNCoedNInner Regional Australia-36.064177146.932402Rural South and WestAlburyhttps://alburynth-p.schools.nsw.gov.au1927-11-01
School_codeSchool_nameYearComposite_class_countComposite_class_studentsPct_composite_classesPct_composite_class_studentsAttendance_pctICSEA_valueLevel_of_schoolinglatest_year_enrolment_FTEIndigenous_pctLBOTE_pctSelective_schoolOpportunity_classSchool_specialty_typeSchool_subtypePreschool_indDistance_educationIntensive_english_centreSchool_genderLate_opening_schoolASGS_remotenessLatitudeLongitudeOperational_directoratePrincipal_networkWebsiteDate_1st_teacher
132383183Teralba Public School2022510283.30000383.59999888.400002970.0Primary School119.015.06.0Not SelectiveNComprehensiveKinder to Year 6NNNCoedNMajor Cities of Australia-32.962493151.605976Regional NorthLake Macquarie Westhttps://teralba-p.schools.nsw.gov.au1884-09-01
132393263Tucabia Public School2022332100.000000100.00000084.099998859.0Primary School35.029.0npNot SelectiveNComprehensiveKinder to Year 6NNNCoedNOuter Regional Australia-29.666698153.106575Rural NorthGraftonhttps://tucabia-p.schools.nsw.gov.au1891-01-01
132404576William Dean Public School2022922860.00000068.30000387.800003994.0Primary School339.04.050.0Not SelectiveNComprehensiveKinder to Year 6NNNCoedNMajor Cities of Australia-33.734259150.858693Metropolitan NorthQuakers Hillhttps://williamdea-p.schools.nsw.gov.au1988-01-01
132413559Yetman Public School2022114100.000000100.00000084.699997944.0Primary School15.0np0.0Not SelectiveNComprehensiveKinder to Year 6NNNCoedYOuter Regional Australia-28.902219150.776261Rural NorthNorthern Tablelandshttps://yetman-p.schools.nsw.gov.au1867-01-01
132421628Cooma Public School20229206100.000000100.00000087.199997985.0Primary School215.05.012.0Not SelectiveNComprehensiveKinder to Year 6NNNCoedNInner Regional Australia-36.237474149.125034Rural South and WestEden-Monarohttps://cooma-p.schools.nsw.gov.au1863-03-01
132431844Enfield Public School2022615160.00000063.70000189.3000031073.0Primary School246.0np67.0Not SelectiveNComprehensiveKinder to Year 6NNNCoedNMajor Cities of Australia-33.888598151.094528Metropolitan SouthStrathfieldhttps://enfield-p.schools.nsw.gov.au1924-01-01
132442197Hurstville Public School20221232.3000002.10000090.6999971098.0Primary School1150.0np98.0Not SelectiveYComprehensiveKinder to Year 6NNNCoedNMajor Cities of Australia-33.964695151.110728Metropolitan SouthGeorges Riverhttps://hurstville-p.schools.nsw.gov.au1876-10-01
132454049Mount Kuring-gai Public School20227164100.000000100.00000088.1999971078.0Primary School168.00.024.0Not SelectiveNComprehensiveKinder to Year 6NNNCoedNMajor Cities of Australia-33.657474151.135604Regional NorthMooney Mooneyhttps://mtkuringga-p.schools.nsw.gov.au1957-01-01
132462983Rocky River Public School2022240100.000000100.00000087.400002964.0Primary School39.023.0npNot SelectiveNComprehensiveKinder to Year 6NNNCoedNOuter Regional Australia-30.612856151.491788Rural NorthArmidalehttps://rockyriver-p.schools.nsw.gov.au1861-01-01
132474225Tulloona Public School202218100.000000100.00000092.599998NaNPrimary School6.0100.00.0Not SelectiveNComprehensiveKinder to Year 6NNNCoedYOuter Regional Australia-28.868872150.101300Rural NorthBarwonhttps://tulloona-p.schools.nsw.gov.au1959-01-01